Cleanlab is an AI trust and data-quality company that helps enterprises detect and remediate errors in their training data and the responses generated by AI agents. The platform builds on a body of academic research called confident learning, which provides statistically grounded methods for identifying mislabeled examples, low-quality data, and uncertain model outputs at scale.
The core product, the Trustworthy Language Model and accompanying agent-supervision platform, scores every AI response with a confidence value and routes low-confidence cases to human reviewers through a no-code workflow. Non-technical teams can then correct outputs, fix knowledge-base content, and feed improvements back into the underlying system, reducing hallucinations and compliance risk in high-stakes deployments like customer support and internal copilots.
Cleanlab was founded in 2021 by three computer-science PhDs from MIT, with co-founder and CEO Curtis Northcutt leading the company. The team open-sourced the original cleanlab Python library, which grew into one of the most widely used data-quality tools in the machine-learning community and seeded the company's enterprise offering.
Cleanlab has raised approximately $30 million in total funding. The most recent round was a $25 million Series A in October 2023, co-led by Menlo Ventures and TQ Ventures, with participation from existing investor Bain Capital Ventures and new investor Databricks Ventures. Menlo's Matt Murphy and TQ's Schuster Tanger joined the board.
The platform is used by Fortune 500 enterprises, AI labs, and ML teams at companies that need to ship LLM applications without exposing customers to hallucinated or non-compliant answers. Competitors include Galileo, Arize, and Patronus AI, but Cleanlab differentiates through its academic roots in confident learning and its tight coupling between data-quality remediation and live agent supervision.